---
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base-finetuned-kinetics
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-kinetics-finetuned-nba-binary-data-2-batch-50-epochs-new-database
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# videomae-base-finetuned-kinetics-finetuned-nba-binary-data-2-batch-50-epochs-new-database

This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1744
- Accuracy: 0.965

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 10000

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6618        | 0.02  | 200   | 0.6293          | 0.6875   |
| 0.5781        | 1.02  | 400   | 1.4660          | 0.6042   |
| 0.8554        | 2.02  | 600   | 0.8740          | 0.6667   |
| 0.4445        | 3.02  | 800   | 1.0660          | 0.6667   |
| 0.3265        | 4.02  | 1000  | 0.6635          | 0.7708   |
| 0.5417        | 5.02  | 1200  | 0.4705          | 0.8542   |
| 0.5912        | 6.02  | 1400  | 1.0082          | 0.7708   |
| 0.5918        | 7.02  | 1600  | 2.6292          | 0.5625   |
| 0.8992        | 8.02  | 1800  | 0.8514          | 0.7708   |
| 0.172         | 9.02  | 2000  | 0.4568          | 0.875    |
| 0.493         | 10.02 | 2200  | 0.7354          | 0.7917   |
| 0.3622        | 11.02 | 2400  | 1.0386          | 0.7708   |
| 0.4966        | 12.02 | 2600  | 0.8979          | 0.7917   |
| 0.3541        | 13.02 | 2800  | 0.8220          | 0.7708   |
| 0.5386        | 14.02 | 3000  | 1.0256          | 0.7708   |
| 0.4615        | 15.02 | 3200  | 1.0447          | 0.7917   |
| 0.1624        | 16.02 | 3400  | 0.6448          | 0.8542   |
| 1.0388        | 17.02 | 3600  | 0.9992          | 0.7708   |
| 0.0442        | 18.02 | 3800  | 1.1626          | 0.7708   |
| 0.2449        | 19.02 | 4000  | 0.8174          | 0.8542   |
| 0.3024        | 20.02 | 4200  | 0.8500          | 0.7917   |
| 0.4879        | 21.02 | 4400  | 1.2219          | 0.7292   |
| 0.4035        | 22.02 | 4600  | 0.6436          | 0.8333   |
| 0.0334        | 23.02 | 4800  | 0.7433          | 0.8333   |
| 0.4849        | 24.02 | 5000  | 0.9911          | 0.8125   |
| 0.6075        | 25.02 | 5200  | 1.2249          | 0.7083   |
| 0.3441        | 26.02 | 5400  | 0.8563          | 0.8333   |
| 0.5653        | 27.02 | 5600  | 0.4557          | 0.8958   |
| 0.196         | 28.02 | 5800  | 0.4156          | 0.8542   |
| 0.0038        | 29.02 | 6000  | 0.4562          | 0.8542   |
| 0.2696        | 30.02 | 6200  | 0.8153          | 0.7917   |
| 0.0015        | 31.02 | 6400  | 0.5923          | 0.8958   |
| 0.0036        | 32.02 | 6600  | 0.7343          | 0.875    |
| 0.3623        | 33.02 | 6800  | 0.3089          | 0.9375   |
| 0.2142        | 34.02 | 7000  | 0.6142          | 0.8958   |
| 0.0008        | 35.02 | 7200  | 0.6010          | 0.875    |
| 0.0005        | 36.02 | 7400  | 0.6238          | 0.875    |
| 0.0002        | 37.02 | 7600  | 0.5966          | 0.875    |
| 0.5           | 38.02 | 7800  | 0.6371          | 0.8542   |
| 0.0004        | 39.02 | 8000  | 0.8515          | 0.8542   |
| 0.0001        | 40.02 | 8200  | 0.5120          | 0.875    |
| 0.0069        | 41.02 | 8400  | 0.8686          | 0.8542   |
| 0.0002        | 42.02 | 8600  | 0.8801          | 0.8542   |
| 0.0001        | 43.02 | 8800  | 0.8996          | 0.8542   |
| 0.0067        | 44.02 | 9000  | 0.7670          | 0.8542   |
| 0.0001        | 45.02 | 9200  | 0.9936          | 0.8333   |
| 0.0638        | 46.02 | 9400  | 0.6616          | 0.875    |
| 0.0001        | 47.02 | 9600  | 0.7978          | 0.8542   |
| 0.0001        | 48.02 | 9800  | 0.6737          | 0.8542   |
| 0.0001        | 49.02 | 10000 | 0.5887          | 0.875    |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3